In the quantitative estimation of the effectiveness of pesticides and repellents, Abbott's correction and similar methods have been used for nearly a century. However, the formulas for such corrections have some disadvantages in performing range estimations and being used in batched experiments. These disadvantages also exist in the estimation of EC₅₀, the chemical concentration that produces 50% mortality. In this study, I propose as a solution to these problems a Bayesian estimation with a logistic model. This method enables the flexible modeling of non-normal variables in complex experimental design, for example, life-death response in a batched experiment. Furthermore, if any knowledge about focal phenomena exists, it can be used in analyses as a prior distribution, thus enhancing the accuracy of the estimation.